SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 1011–1020 of 3874 papers

TitleStatusHype
Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution ReconstructionCode1
Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration NetworkCode1
Diffusion Prior Interpolation for Flexibility Real-World Face Super-ResolutionCode1
Underwater Image Super-Resolution using Deep Residual MultipliersCode1
Multi-Depth Branch Network for Efficient Image Super-ResolutionCode1
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-ResolutionCode1
MoTIF: Learning Motion Trajectories with Local Implicit Neural Functions for Continuous Space-Time Video Super-ResolutionCode1
Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolutionCode1
Boosting Single Image Super-Resolution via Partial Channel ShiftingCode1
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain ImagingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified